Abstract One of the key strategies to ending the HIV epidemic in the United States is to increase investments in geographic hotspots, or areas of elevated disease burden. However, a few key challenges limit the ability for hotspot mapping to inform effective HIV interventions. First, hotspot mapping typically uses residential address of an individual with an incident infection. Yet for many individuals, potential risky sex and drug-use behaviors do not occur at home. Identifying the places in which risk occurs, as opposed to where an individual who engages in risk lives, will help target and increase exposure to place-based interventions. Second, individual behavior is often constrained or dictated by larger socio-ecological factors, and these can work together to create or maintain health disparities. Interventions tailored to risk hotspots to improve the HIV care cascade need to be responsive to the broader socio-ecological context of the hotspot. Third, an implicit assumption about prioritizing hotspots for interventions is that efforts to reduce incidence in hotspots will indirectly reduce incidence elsewhere as well, as infections likely spread from high incidence areas to neighboring lower prevalence areas. Information on potential geographic and sexual network connectivity between hotspots and other places can inform prioritization of risk hotspots for HIV interventions. We will demonstrate innovative methods to identify and prioritize HIV risk hotspots among urban, non-white men who have sex with men (MSM), populations at especially high-risk for HIV in the United States. We will leverage and supplement existing longitudinal data on non-White MSM, both HIV-infected and uninfected, from the ongoing mStudy cohort in Los Angeles, to achieve our goals. We will first adapt an existing web-based activity space survey of HIV risk and prevention to incorporate geographic characteristics of networks, residential mobility, and locations of drug use. Then we will use cognitive interviews to validate the survey instrument, and collect data from 250 MSM using the updated survey. Second, we will identify and describe HIV transmission and acquisition risk hotspots within Los Angeles County, using individual activity space data on sexual risk and drug use locations, neighborhood characteristics of the hotspots, and sociodemographic characteristics and behaviors of individuals engaging in risk in the hotspots. Third, we will estimate connectivity of hotspots to other locations (e.g., other hotspots, residential locations, and prevention locations), in order to identify which hotspot to prioritize for maximizing intervention effectiveness over time and space. This work will improve the effectiveness of HIV prevention, care, and treatment strategies in two ways. First, it will guide decision making as to which hotspot to prioritize, by ranking them in terms of potential connectivity to other places, and thus maximizing the reach of the intervention. Second, it will inform which combination of interventions will be most successful, by characterizing the context and composition of the hotspot.